{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "<p align=\"center\">\n",
    "    <img src=\"https://github.com/GeostatsGuy/GeostatsPy/blob/master/TCG_color_logo.png?raw=true\" width=\"220\" height=\"240\" />\n",
    "\n",
    "</p>\n",
    "\n",
    "## Data Science Basics in Python\n",
    "\n",
    "### Bootstrap for Uncertainty Models \n",
    "\n",
    "#### Michael Pyrcz, Associate Professor, University of Texas at Austin \n",
    "\n",
    "##### [Twitter](https://twitter.com/geostatsguy) | [GitHub](https://github.com/GeostatsGuy) | [Website](http://michaelpyrcz.com) | [GoogleScholar](https://scholar.google.com/citations?user=QVZ20eQAAAAJ&hl=en&oi=ao) | [Book](https://www.amazon.com/Geostatistical-Reservoir-Modeling-Michael-Pyrcz/dp/0199731446) | [YouTube](https://www.youtube.com/channel/UCLqEr-xV-ceHdXXXrTId5ig)  | [LinkedIn](https://www.linkedin.com/in/michael-pyrcz-61a648a1)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Bootstrap in Python \n",
    "\n",
    "Here's a simple workflow, demonstration of bootstrap for modeling workflows. This should help you get started with this important data analytics method to evaluate and integrate uncertainty for any statistic or model.  \n",
    "\n",
    "#### Bootstrap\n",
    "\n",
    "Uncertainty in statistics\n",
    "* one source of uncertainty is the paucity of data.\n",
    "* do 200 or even less samples provide a precise (and accurate estimate) of the mean? standard deviation? skew? P13?\n",
    "\n",
    "Would it be useful to know the uncertainty in these statistics due to limited sampling?\n",
    "* what is the impact of uncertainty in the mean porosity e.g. 20%+/-2%?\n",
    "* empirically calculate standard errors, confidence intervals and to conduct hypothesis testing\n",
    "\n",
    "**Bootstrap** is a method to assess the uncertainty in a statistic by repeated random sampling with replacement to create multiple simulated data sets.\n",
    "\n",
    "#### The Bootstrap Workflow (Efron, 1982)\n",
    "\n",
    "Statistical resampling procedure to calculate uncertainty in a calculated statistic from the data itself.\n",
    "* Does this work?  Prove it to yourself, for uncertainty in the mean solution is standard error: \n",
    "\n",
    "\\begin{equation}\n",
    "\\sigma^2_\\overline{x} = \\frac{\\sigma^2_s}{n}\n",
    "\\end{equation}\n",
    "\n",
    "Extremely powerful - could calculate uncertainty in any statistic!  e.g. P13, skew etc.\n",
    "* Would not be possible access general uncertainty in any statistic without bootstrap.\n",
    "* Advanced forms account for spatial information and sampling strategy (game theory and Journel’s spatial bootstrap (1993).\n",
    "\n",
    "Steps: \n",
    "\n",
    "&nbsp;&nbsp;**1.** assemble a representative sample set or build a cumulative distribution function (CDF) using analog data and/or declustering weights\n",
    "\n",
    "&nbsp;&nbsp;**2.** For $\\ell = 1, \\ldots, L$ realizations, do the following:\n",
    "\n",
    "&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;For $i = \\alpha, \\ldots, n$ samples, do the following:\n",
    "\n",
    "&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Draw a random sample with replacement from the sample set or Monte Carlo simulate from the CDF. \n",
    "\n",
    "&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Calculate a realization of the statistic from the $n$ samples, e.g. $m^\\ell$, $\\sigma^2_{\\ell}$.\n",
    "\n",
    "&nbsp;&nbsp;**3.** Compile and summarize the $L$ realizations of the statistic of interest.\n",
    "\n",
    "This is a very powerful method.  Let's try it out. But first...\n",
    "\n",
    "#### Bootstrap Assumptions and Limitations\n",
    "\n",
    "* **representativity** - samples are sufficient, representative\n",
    "* **stationarity** - metric of interest is invariant under translation (identically distributed)\n",
    "* **no spatial context** - independent samples (no between sample correlation), does not account for boundary of area of interest and local information sources\n",
    "* **uncertainty source** - only accounts for uncertainty due to too few samples (no uncertainty due nonstationarity, multiple populations or measurement errors)\n",
    "\n",
    "\n",
    "#### Project Goal\n",
    "\n",
    "Learn the basics for working with bootstrap for Uncertainty Modeling in Python to build practical spatial data analytics, geostatistics and machine learning workflows.\n",
    "\n",
    "* Focus on customization and not a survey of available plot times\n",
    "\n",
    "#### Caveats\n",
    "\n",
    "I included methods that I have found useful for building my science and engineering workflows for subsurface modeling. \n",
    "\n",
    "* Accessibility for most scientists and engineers is my goal. \n",
    "* There are more advanced, more compact, more efficient methods to accomplish the same tasks. \n",
    "* I appreciate feedback and I will use it to improve these walk-throughs.\n",
    "\n",
    "#### Load and Configure the Required Libraries\n",
    "\n",
    "The following code loads the required libraries and sets a plotting default."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np                        # ndarrys for gridded data\n",
    "import pandas as pd                       # DataFrames for tabular data\n",
    "import os                                 # set working directory, run executables\n",
    "import matplotlib.pyplot as plt           # for plotting\n",
    "import matplotlib\n",
    "from scipy import stats                   # summary statistics\n",
    "import math                               # trig etc.\n",
    "import scipy.signal as signal             # kernel for moving window calculation\n",
    "import random                             # random sampling\n",
    "from scipy.stats import gaussian_kde      # for PDF calculation\n",
    "from scipy.stats import t                 # Student's t distribution for analytical solution\n",
    "import seaborn as sns                     # for advanced plotting"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Convenience Functions for Plotting\n",
    "\n",
    "These are convenience functions to visualize the data and bootstrap results.\n",
    "* **custom_histogram** - data histogram with summary statistics \n",
    "* **custom_histogram_with_uncert** - data histogram with boostrap uncertainty model \n",
    "* **display_bootstrap** - bootstrap realizations' histogram with analytical or empirical PDF\n",
    "\n",
    "I include these for concise, readable workflows."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def custom_histogram(zdata,zmin,zmax,xlabel,title):\n",
    "    offset = (zmax-zmin)/30.0\n",
    "    freq = plt.hist(zdata,color = 'red',alpha = 0.3,edgecolor='black',bins = np.linspace(zmin,zmax,int(len(zdata)/3)))[0]\n",
    "    ht = np.max(freq)\n",
    "    plt.axvline(x=np.average(zdata),linestyle=\"--\",c='black')\n",
    "    plt.text(np.average(zdata)+offset,ht*0.95, r'Average = ' + str(round(np.average(zdata),1)), fontsize=12)\n",
    "    plt.text(np.average(zdata)+offset,ht*0.90, r'St.Dev. = ' + str(round(np.std(zdata),1)), fontsize=12)\n",
    "    plt.text(np.average(zdata)+offset,ht*0.85, r'P10 = ' + str(round(np.percentile(zdata,10),1)), fontsize=12)\n",
    "    plt.text(np.average(zdata)+offset,ht*0.80, r'P90 = ' + str(round(np.percentile(zdata,90),1)), fontsize=12)\n",
    "    plt.xlabel(xlabel); plt.ylabel('Frequency'); plt.title(title)\n",
    "     \n",
    "def custom_histogram_with_uncert(zreal,zmin,zmax,zdata,xlabel,title):\n",
    "    offset = (zmax-zmin)/30.0\n",
    "    freq = plt.hist(zdata,color = 'red',alpha = 0.2,edgecolor='grey',bins = np.linspace(zmin,zmax,int(len(zdata)/3)))[0]\n",
    "    ht = np.max(freq)\n",
    "    plt.axvline(x=np.average(zdata),c='black')\n",
    "    plt.axvline(x=np.percentile(zreal,90),linestyle=\"--\",c='black')\n",
    "    plt.axvline(x=np.percentile(zreal,10),linestyle=\"--\",c='black')\n",
    "    plt.text(np.percentile(zreal,90)+offset,ht*0.90, r'Average = ' + str(round(np.average(zreal),1)), fontsize=12)\n",
    "    plt.text(np.percentile(zreal,90)+offset,ht*0.85, r'Average P90 = ' + str(round(np.percentile(zreal,90),1)), fontsize=12)\n",
    "    plt.text(np.percentile(zreal,90)+offset,ht*0.80, r'Average P10 = ' + str(round(np.percentile(zreal,10),1)), fontsize=12)\n",
    "    plt.xlabel(xlabel); plt.ylabel('Frequency'); plt.title(title)\n",
    "\n",
    "def display_bootstrap(zreal,zmin,zmax,zdata,title,abin,analytical,stat):\n",
    "    offset = (zmax-zmin)/30.0\n",
    "    freq = plt.hist(zreal,color = 'red',alpha = 0.2,edgecolor = 'black',bins=np.linspace(zmin,zmax,100),label = 'bootstrap',density = True)[0] # plot the distribution, could also calculate any summary statistics\n",
    "    ht = np.max(freq)\n",
    "    if analytical is None:\n",
    "        sns.kdeplot(x=zreal,color = 'grey',alpha = 0.1,levels = 1,bw_adjust = 1,label='bootstrap PDF') \n",
    "        plt.xlabel('Boostrap Realizations and Kernel Density Estimate') \n",
    "    else:    \n",
    "        plt.plot(abin,analytical,color = 'black',label = 'analytical',alpha=0.4)\n",
    "        plt.fill_between(abin, 0, analytical, where = abin <= np.percentile(zreal,10), facecolor='red', interpolate=True, alpha = 0.5)\n",
    "        plt.fill_between(abin, 0, analytical, where = abin >= np.percentile(zreal,90), facecolor='red', interpolate=True, alpha = 0.5)\n",
    "        plt.xlabel('Boostrap Realizations and Analytical Sampling Distributions') \n",
    "    plt.axvline(x=stat(zdata),linestyle=\"--\",c='black')\n",
    "    plt.text(stat(zdata)+offset,ht*0.95, r'Average = ' + str(round(np.average(zreal),1)), fontsize=12)\n",
    "    plt.text(stat(zdata)+offset,ht*0.90, r'St.Dev. = ' + str(round(np.std(zreal),1)), fontsize=12)\n",
    "    plt.text(stat(zdata)+offset,ht*0.85, r'P90 = ' + str(round(np.percentile(zreal,90),1)), fontsize=12)\n",
    "    plt.text(stat(zdata)+offset,ht*0.8, r'P10 = ' + str(round(np.percentile(zreal,10),1)), fontsize=12)\n",
    "    plt.ylabel('Frequency'); plt.title(title)\n",
    "    plt.legend(loc = 'upper left')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Set the Random Number Seed\n",
    "\n",
    "Since we will be random sampling, Monte Carlo simulation for sampling with replacement, we set the random number seed for worklfow repeatability\n",
    "\n",
    "* note, if you rerun any bootstrap code blocks the results will change unless the seed is set in each code block"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "seed = 13                                 # random number generator seed \n",
    "random.seed(a=seed)                       # initialize the random number generator  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Set the working directory\n",
    "\n",
    "I always like to do this so I don't lose files and to simplify subsequent read and writes (avoid including the full address each time). "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "os.chdir(\"c:/PGE383\")                     # set the working directory"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Load the Tabular Data\n",
    "\n",
    "Here's the command to load our comma delimited data file in to a Pandas' DataFrame object.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(r'https://raw.githubusercontent.com/GeostatsGuy/GeoDataSets/master/sample_data_biased.csv') # load the data from Dr. Pyrcz's github repository\n",
    "#df = pd.read_csv('sample_data_biased.csv') # load our data table from the working directory"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Feature Engineering\n",
    "\n",
    "We do just a couple basic things:\n",
    "\n",
    "* let's drop some samples so that we increase the variations in bootstrap samples for our demonstration below.\n",
    "\n",
    "* let's convert porosity from fraction to percentage (100x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using 29 number of samples\n"
     ]
    }
   ],
   "source": [
    "fraction = 0.1                             # set proportion of data to retain and observe the uncertainty \n",
    "df['Porosity'] = df['Porosity']*100        # convert fraction to percentage\n",
    "df = df.sample(frac = fraction,random_state=seed) # extract a random subset  \n",
    "print('Using ' + str(len(df)) + ' number of samples')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Visualize the DataFrame\n",
    "\n",
    "Visualizing the DataFrame would be useful and we already learned about these methods in this demo (https://git.io/fNgRW)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>X</th>\n",
       "      <th>Y</th>\n",
       "      <th>Facies</th>\n",
       "      <th>Porosity</th>\n",
       "      <th>Perm</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>107</th>\n",
       "      <td>390</td>\n",
       "      <td>779</td>\n",
       "      <td>1</td>\n",
       "      <td>19.328012</td>\n",
       "      <td>495.525997</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>209</th>\n",
       "      <td>530</td>\n",
       "      <td>789</td>\n",
       "      <td>1</td>\n",
       "      <td>15.487071</td>\n",
       "      <td>51.991055</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>800</td>\n",
       "      <td>800</td>\n",
       "      <td>1</td>\n",
       "      <td>12.783107</td>\n",
       "      <td>3.294085</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>207</th>\n",
       "      <td>450</td>\n",
       "      <td>689</td>\n",
       "      <td>1</td>\n",
       "      <td>15.476265</td>\n",
       "      <td>82.943101</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>221</th>\n",
       "      <td>490</td>\n",
       "      <td>959</td>\n",
       "      <td>1</td>\n",
       "      <td>19.656596</td>\n",
       "      <td>403.008633</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       X    Y  Facies   Porosity        Perm\n",
       "107  390  779       1  19.328012  495.525997\n",
       "209  530  789       1  15.487071   51.991055\n",
       "30   800  800       1  12.783107    3.294085\n",
       "207  450  689       1  15.476265   82.943101\n",
       "221  490  959       1  19.656596  403.008633"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()                                 # display first 4 samples in the table as a preview"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Calculate Summary Statistics\n",
    "\n",
    "The data includes **X** and **Y** coordinates (meters), **Facies** 1 and 0 (1 is sandstone and 0 interbedded sand and mudstone), **Porosity** (fraction), and **Permeability** as Perm (mDarcy). "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>X</th>\n",
       "      <td>29.0</td>\n",
       "      <td>463.103448</td>\n",
       "      <td>246.520116</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>300.000000</td>\n",
       "      <td>430.000000</td>\n",
       "      <td>550.000000</td>\n",
       "      <td>900.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Y</th>\n",
       "      <td>29.0</td>\n",
       "      <td>563.137931</td>\n",
       "      <td>325.808524</td>\n",
       "      <td>89.000000</td>\n",
       "      <td>199.000000</td>\n",
       "      <td>619.000000</td>\n",
       "      <td>839.000000</td>\n",
       "      <td>959.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Facies</th>\n",
       "      <td>29.0</td>\n",
       "      <td>0.896552</td>\n",
       "      <td>0.309934</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Porosity</th>\n",
       "      <td>29.0</td>\n",
       "      <td>13.943832</td>\n",
       "      <td>3.584357</td>\n",
       "      <td>7.996248</td>\n",
       "      <td>11.684271</td>\n",
       "      <td>12.740454</td>\n",
       "      <td>15.487071</td>\n",
       "      <td>21.821727</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Perm</th>\n",
       "      <td>29.0</td>\n",
       "      <td>155.973329</td>\n",
       "      <td>376.973510</td>\n",
       "      <td>0.111985</td>\n",
       "      <td>3.967875</td>\n",
       "      <td>11.806496</td>\n",
       "      <td>98.144295</td>\n",
       "      <td>1954.288198</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          count        mean         std        min         25%         50%  \\\n",
       "X          29.0  463.103448  246.520116  20.000000  300.000000  430.000000   \n",
       "Y          29.0  563.137931  325.808524  89.000000  199.000000  619.000000   \n",
       "Facies     29.0    0.896552    0.309934   0.000000    1.000000    1.000000   \n",
       "Porosity   29.0   13.943832    3.584357   7.996248   11.684271   12.740454   \n",
       "Perm       29.0  155.973329  376.973510   0.111985    3.967875   11.806496   \n",
       "\n",
       "                 75%          max  \n",
       "X         550.000000   900.000000  \n",
       "Y         839.000000   959.000000  \n",
       "Facies      1.000000     1.000000  \n",
       "Porosity   15.487071    21.821727  \n",
       "Perm       98.144295  1954.288198  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe().transpose()                 # summary statistics         "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Basic Data Visualization Parameters\n",
    "\n",
    "Let's set the areal extents (X and Y), feature extents and color map."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "xmin = 0.0; xmax = 1000.0                 # limits for X coordinate\n",
    "ymin = 0.0; ymax = 1000.0                 # limits for Y coordinate\n",
    "pormin = 5.0; pormax = 25.0;              # limits for Porosity feature\n",
    "permmin = 0.01; permmax = 10000           # limits for Permeability feature\n",
    "cmap = plt.cm.inferno                     # color map"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Basic Data Visualization\n",
    "\n",
    "Let's try out location maps, histograms and scatter plots. See my other live code walk-throughs for more information."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-10-9feb46d03f89>:10: MatplotlibDeprecationWarning: Passing parameters norm and vmin/vmax simultaneously is deprecated since 3.3 and will become an error two minor releases later. Please pass vmin/vmax directly to the norm when creating it.\n",
      "  plt.scatter(df['X'],df['Y'],s = 20,c = df['Perm'],cmap = cmap,linewidths = 0.3,edgecolor ='black',alpha =0.8,vmin=permmin,vmax=permmax,norm=matplotlib.colors.LogNorm())\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 7 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.subplot(231)\n",
    "plt.scatter(df['X'],df['Y'],s=20,c=df['Porosity'],cmap=cmap,linewidths=0.3,edgecolor='black',alpha=0.8,vmin=pormin,vmax=pormax)\n",
    "plt.colorbar(); plt.xlabel('X (m)'); plt.ylabel('Y (m)'); plt.title('Porosity Location Map')\n",
    "\n",
    "plt.subplot(234)\n",
    "plt.hist(df['Porosity'],color='red',alpha=0.3,edgecolor='black',bins=np.linspace(pormin,pormax,int(len(df)/3)))\n",
    "plt.xlabel('Porosity (fraction)'); plt.ylabel('Frequency'); plt.title('Porosity Histogram')\n",
    "\n",
    "plt.subplot(232)\n",
    "plt.scatter(df['X'],df['Y'],s = 20,c = df['Perm'],cmap = cmap,linewidths = 0.3,edgecolor ='black',alpha =0.8,vmin=permmin,vmax=permmax,norm=matplotlib.colors.LogNorm())\n",
    "plt.colorbar(); plt.xlabel('X (m)'); plt.ylabel('Y (m)'); plt.title('Permeability Location Map')\n",
    "\n",
    "plt.subplot(235)\n",
    "plt.hist(df['Perm'],color='red',alpha=0.3,edgecolor='black',bins=np.logspace(np.log10(permmin),np.log10(permmax),int(len(df)/3)))\n",
    "plt.xlabel('Permeability (mD)'); plt.ylabel('Frequency'); plt.title('Permeability Histogram'); plt.xscale('log')\n",
    "\n",
    "plt.subplot(233)\n",
    "plt.scatter(df['Porosity'],df['Perm'],s=20,color='red',alpha=0.3,edgecolor='black')\n",
    "plt.ylabel('Permeability (mD)'); plt.xlabel('Porosity (fraction)'); plt.title('Permeability-Porosity Scatter Plot')\n",
    "plt.yscale('log')\n",
    "sns.kdeplot(x=df['Porosity'],y=df['Perm'],color = 'black',alpha=0.2,levels=4)\n",
    "\n",
    "plt.subplots_adjust(left=0.0, bottom=0.0, right=3.0, top=2.2, wspace=0.2, hspace=0.2)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Bootstrap Method in Python                       \n",
    "\n",
    "If you are new to bootstrap and Python, here's a simple pseudocode workflow for bootstrap.\n",
    "\n",
    "* specify the number of bootstrap realizations, $L$\n",
    "* declare a list to store the bootstrap realizations of the statistic of interest\n",
    "* loop over $L$ bootstrap realizations\n",
    "    * $n$ MCS, random samples with replacement for a new realization of the data\n",
    "    * calculate the realization of the statistic from the realization of the data\n",
    "* summarize the resulting uncertainty model, histogram, summary statistics etc.\n",
    "\n",
    "#### Bootstrap of the Sample Mean, Arithmetic Average\n",
    "\n",
    "Let's make a simple bootstrap function where:\n",
    "* **zdata** is a 1D ndarray of the original data\n",
    "* **nreal** is the number of bootstrap realizations to calculate\n",
    "* **stat** is a function that calculates the statistic"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "def bootstrap(zdata,nreal,stat):\n",
    "    zreal = []                            # declare an empty list to store the bootstrap realizations\n",
    "    for l in range(0,nreal):              # loop over the L bootstrap realizations\n",
    "        samples = random.choices(zdata, k=len(zdata)) # n Monte Carlo simulations, sample with replacement\n",
    "        zreal.append(stat(samples))       # calculate the realization of the statistic and append to list\n",
    "    return zreal                          # return the list of realizations of the statistic"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Additional Declarations for Read-able Code\n",
    "\n",
    "Let's add a couple more declarations for concise, read-able code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "por = df['Porosity'].values               # copy DataFrame series, Porosity feature, to 1D ndarray\n",
    "lpor = np.linspace(pormin,pormax,100)     # calculate bins for plotting"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Demonstration of Bootstrap for Uncertainty in the Mean\n",
    "\n",
    "Let's bootstrap to calculate the uncertainty in the mean. We will compare the bootstrap results with the analytical expression:\n",
    "\n",
    "\\begin{equation}\n",
    "\\mu \\sim \\overline{x} \\pm t_{\\frac{\\alpha}{2},n-1}\\sqrt{\\frac{s^2}{n}}\n",
    "\\end{equation}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "L = 1000; random.seed(a=seed)             # set the number and seed for the bootstrap realizations   \n",
    "por_avg_real = bootstrap(por,L,np.average) # bootstrap of the arithmetic average \n",
    "\n",
    "plt.subplot(131)                          # plot the original data\n",
    "custom_histogram(por,pormin,pormax,'Porosity (fraction)','Original Porosity Histogram')\n",
    "plt.subplot(132)                          # plot the bootstrap results\n",
    "mod = t.pdf(lpor,len(df)-1,loc=np.average(por),scale=math.sqrt(np.var(por)/len(df))) # analytical PDF\n",
    "display_bootstrap(por_avg_real,pormin,pormax,por,'Bootstrap Average Realizations',lpor,mod,np.average)\n",
    "plt.subplot(133)                          # plot the original data and bootstrap results \n",
    "custom_histogram_with_uncert(por_avg_real,pormin,pormax,por,'Porosity (%)','Bootstrap Uncertainty Average')\n",
    "plt.subplots_adjust(left=0.0, bottom=0.0, right=3.0, top=1.2, wspace=0.2, hspace=0.2); plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Optional: Write out the Bootstrap Samples for Uncertainty in the Mean\n",
    "\n",
    "Set the file name and feature name and write your bootstrap realizations as a .csv file.\n",
    "\n",
    "* file will be written in the current working directory"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>avg_por_boot</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>13.505598</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>13.896181</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>13.771810</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>13.872079</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>14.120102</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   avg_por_boot\n",
       "0     13.505598\n",
       "1     13.896181\n",
       "2     13.771810\n",
       "3     13.872079\n",
       "4     14.120102"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "filename = 'avg_por_boot.csv'             # specify file name and feature name for writing out\n",
    "featurename = 'avg_por_boot'\n",
    "\n",
    "df_boot = pd.DataFrame(data=por_avg_real,columns=[featurename]) # DataFrame with the bootstrap realizations\n",
    "df_boot.to_csv(filename,index=False)      \n",
    "df_boot.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Demonstration of Bootstrap for Uncertainty in the Standard Deviation\n",
    "\n",
    "Let's perform bootstrap of a measure of dispersion, the standard deviation, $\\sigma_x$."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "L = 1000; random.seed(a=seed)             # set the number and seed for the bootstrap realizations   \n",
    "por_stdev_real = bootstrap(por,L,np.std)  # bootstrap of the standard deviation\n",
    "\n",
    "plt.subplot(131)                          # plot the bootstrap results                       \n",
    "display_bootstrap(por_stdev_real,0,7,por,'Bootstrap Standard Deviation',lpor,None,np.std)\n",
    "plt.subplots_adjust(left=0.0, bottom=0.0, right=3.0, top=1.1, wspace=0.2, hspace=0.2); plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Bootstrap of the Coefficient of Variation\n",
    "\n",
    "Here's a statistic that is derived from multiple statistics for each bootstrap realization of the data.\n",
    "\n",
    "* $CV_x^{\\ell} = \\frac{s_x^{\\ell}}{\\overline{x}_x^{\\ell}}$ \n",
    "\n",
    "* this reinforces that we bootstrap dataset realizations and then calculate the statistic on this dataset realization\n",
    "\n",
    "For the coefficient of variation we will:\n",
    "    \n",
    "* calculate a bootstrap realization of the dataset with $n$ samples with replacement\n",
    "* calculate the mean and standard deviation from this bootstrapped realization of the dataset\n",
    "* calculate a boostrap realization of the coefficient of variation as the standard deviation realizaiton divided by the mean realization\n",
    "\n",
    "Repeat this $L$ time on $L$ realizations of the data and then evaluate the resulting distribution.\n",
    "\n",
    "#### Demonstration of Bootstrap for Uncertainty in the Coefficient of Variation\n",
    "\n",
    "Now bootstrap uncertainty of coefficient of variation, $CV_x^{\\ell} = \\frac{s_x^{\\ell}}{\\overline{x}_x^{\\ell}}$ "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "cv = lambda x: np.std(x) / np.mean(x)     # define lambda function to calculate coefficient of variation\n",
    "L = 1000; random.seed(a=seed)             # set the number and seed for the bootstrap realizations   \n",
    "por_cv_real = bootstrap(por,L,cv)         # bootstrap of the interquartile range \n",
    "\n",
    "plt.subplot(131)                          # plot the bootstrap results \n",
    "display_bootstrap(por_cv_real,0,0.5,por,'Bootstrap Coefficient of Variation',lpor,None,cv)\n",
    "plt.subplots_adjust(left=0.0, bottom=0.0, right=3.0, top=1.1, wspace=0.2, hspace=0.2); plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Demonstration of Bootstrap for Uncertainty in the Interquartile Range\n",
    "\n",
    "Here's another statistic that is derived from a combination of statistics, interquartile range, $IQR_x^{\\ell} = F_x^{\\ell}(0.75) - F_x^{\\ell}(0.25)$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "L = 1000; random.seed(a=seed)             # set the number and seed for the bootstrap realizations   \n",
    "por_iqr_real = bootstrap(por,L,stats.iqr) # bootstrap of the interquartile range \n",
    "\n",
    "plt.subplot(131)                          # plot the bootstrap results \n",
    "display_bootstrap(por_iqr_real,0,10,por,'Bootstrap IQR',lpor,None,stats.iqr)\n",
    "plt.subplots_adjust(left=0.0, bottom=0.0, right=3.0, top=1.1, wspace=0.2, hspace=0.2); plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Demonstration of Bootstrap for Uncertainty in the Percentile\n",
    "\n",
    "Now let's select any percentile and bootstrap it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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cdNFFdO7cudS2zp078/vf/542bdqQmZnJ8OHDycvLY9WqVVH3NWXKFP74xz/SoEEDOnXqxGWXXRZzgISaTgksQRI1pUqyp1MRqUnGjRvHBRdcwAUXXMCMGTPYsGED9erV4ze/+Q0TJkwIl5s0aRL9+/dn77335tNPP+XSSy/l2WefZfPmzVx55ZWcccYZxZogJ0yYwFtvvcW2bdvIyspi//335/333+enn37izjvv5MILL+T7778H4LnnnmP69OksXLiQTz/9NDw7Q8jQoUPJyspi9erVfPbZZ8ycOZPnn38+6vt55ZVXaNasWczHN998U+VztnDhQvLy8jjggANilom8fco5V+zHQCpJy16IS+bOJW/btoTtrzpHDXjssce48MILadCgQcwyY8eO5ZBDDqFt27althUWFpKZmRlkiCIVcv311/PjmjW8cscdFXrdBx98wNq1axk8eDAtW7Zk//3355VXXuGGG27g/PPPZ/jw4dx3332AlxiuvPJKwEs4V155JUceeSTgJZj777+fuXPnhke8ue666+jQoUP4WOecc054+dxzz+WBBx7gk08+YeDAgUyaNInf/e53tG/fHoCbb745PD3Mhg0bmD59Otu2bSM7O5uGDRtyww03MHr06HA8kc4//3zOP//8Cp2Hiti+fTsXXXQRd955J02bNo1a5qSTTuLBBx/kxRdfZMOGDYwZM6bcH801VVomsJJ3/VdVvKMGhKZU+eyzzzjooIMYN24cH330ETfddFN4xI1nnnmGevXqMWvWrFLrn3322fB0Ki1btuQ///kPl112GfPnz8fMuPTSS+nQoUN4OpXQSBxdu3bl0ksvZebMmVxzzTXs2LGD0aNHh3+FvfTSSzRo0IBhw4ZRv359li1bxoYNG3j00Uc57bTTEnaeRKJZuHBhpe4De/HFFxkwYAAt/etn559/Pi+++CI33HADv/rVr9i9ezcff/wxrVu3ZuHChZx11lkArF27lhdffJG///3v4X3l5eUVa7WITF7g1fQeffRR1qxZA3jXlTZt2gTA+vXri5WPXF67di35+fm0adMmvK6oqKjU/qvD7t27Of300+nbty+33HJLzHJPPPEE1157LQceeCB77bUXQ4YMKVabTSVqQkygklOqPProoyk9nYpIsuzevZtJkybx3nvv0bp1a1q3bs3IkSNZtGgRixYtIiMjg8GDBzNhwgReeeUVTjvtNBo3bgx4Cea2225j27Zt4ceuXbsYMmRIeP+Rg1ivXbuWK664gieffJLNmzezbds2DjnkkHAzW5s2bVi3bl24/Lfffhte7tChA/Xq1WPTpk3hY23fvr3YDBCRxo8fT6NGjWI+KtuEmJuby5lnnkm7du149tlnyyzbokULxo8fzw8//MCyZcsoKiqiT58+lTpusgWWwMxsjJn9aGZlNq6a2RFmVmhmyRvfKEFKTqkya9aslJxORSTZJk+eTGZmJsuXLw9P0bNixQqOPfbY8Gzf559/PhMnTmT8+PHFmuWuuOIKRo0axccff4xzjp9//pm33nor5gDYP//8M2ZGK7/V5oUXXih2TWjw4ME8/vjjfPfdd2zbto2HHnoovK1NmzYMGDCAG2+8ke3bt1NUVMSXX37Je++9F/VYF1xwATt37oz52HfffaO+rqioiJycHPLz83HOkZOTQ15eHgD5+fkMGjSI7Oxsxo0bR0ZG2V/rX375JZs3b6awsJDp06czevRobr/99jJfU1MFWQMbC5Q52JmZZQIPATMCjKPaRJuaJJqKTqdy3HHH8dRTT3H55ZfHLFtyOpUnn3ySJUuWcOeddxarZcUznYpIsr344otccskl7LvvvuEaWOvWrbnmmmsYP348BQUFHHnkkTRs2JD169cXm7Oud+/ePPfcc1xzzTU0b96cAw44oMxRaLp168aNN97IUUcdxT777MOSJUvCP0TBS4gDBgygR48eHHbYYZxyyilkZWWFrzWPGzeOvLw8unXrRvPmzRk0aFC4A0iizJkzh+zsbE455RS++eYbsrOzGTBgAAAffvghU6dOZebMmTRr1ixcm3v//fcBeP/992nUqFF4XwsWLKB79+40btyYW265hfHjx3PwwQcnNN7qEtg1MOfcHDPrVE6xa4E3gCOCiqM6lZxS5YQTTuDZZ59Ny+lUROJ10EEHsSmi2S0eb0dMvRJp8ODBDB48OPx89erVUcuddNJJnBRjsODQda5I9913X7hDSElZWVmMHDmSkSNHAjB9+nTatm0b/vHXtGlTnnnmGZ555pmY76eqjjvuuJg/fPv371/mj+Jjjz222D1hJc9hKktaJw4zawecBfyKchKYmQ0HhgMxq9g1QckpVR5//HH69u2bNtOpiFTG6NGji80Flmp2797N7NmzGTBgABs2bODuu+8OdxiR5Ap0OhW/BjbVOVfqBikzew14xDk318zG+uVeL2+f8Uynksrd6IOk6VQkWRa8/Xap+cBSZTqVXbt20b9/f1auXEl2djannnoqjz/+eJnXpCVxaup0Kr2BV/1qeEvgFDMrcM5NruqO0yHZiKSL4cOHs+nbb/nnvfcmO5RKadCgAfPmzUt2GBJF0hKYc26/0HJEDWxysuKpDTSdiiTD559/rvnAJBCBJTAzmwAcB7Q0s3XAnUAdAOfcqKCOKyIitUNg3eidc0Occ22cc3Wcc+2dc/9wzo2Klrycc8Piuf5VzvGq8nKpBvobSUV88MEHHH300TRt2pQWLVrQr1+/cFPe2LFjOeaYY8p8fadOncjOzqZx48bhQX1HjRoV7hRVE40cOZLWrVvTtGlTLr300jKnkPnvf//L4YcfTpMmTejcuTOjR4+uxkhrhrQYiaN+/fps3rxZX5A1mHOOzZs3q9ejxGX79u2cdtppXHvttWzZsoXvvvuOO++8k3r16lVoP1OmTGHHjh2sXbuWm2++mYceeojLLrssoKirZsaMGTz44IPMmjWLNWvW8NVXX3HnnXdGLZufn89ZZ53FlVdeyU8//cTEiRP5/e9/z6JFi6o56uRKi7EQ27dvz7p169gY55iFkhz169cPD4gqtUfPnj35Mcq9V2X5/PPPAcLDP0XeuLtixQpGjBhBfn4+jRo1Iisri23l9Dpu2rQpZ5xxBq1bt6Zv377ceOONHHLIIeTm5nLbbbcxadIkcnNzOeussxg5ciTZ2dl07dqVhx9+ODxeaEFBAa1bt2bmzJkcfvjhFTsJcXjxxRe57LLLwjcV33HHHVxwwQU8+OCDpcpu2bIlPHCvmXHEEUfQtWtXli9fzqGHHprw2Gos51xKPXr16uVEJLXMnz7dufnzw4/506eXWf6nn35yLVq0cBdffLGbNm2a27JlS7HtL7zwguvXr1+Z++jYsaN75513Sq3v0KGDe/rpp51zzv3ud79zp59+utu8ebPbvn27O+2009zNN9/snHPu7rvvdueff374dVOnTnVdunQp972+//77rmnTpjEf77//ftTX9ejRw7366qvh5xs3bnSA27RpU9TyQ4YMcU8++aQrKChwH374oWvVqpX75ptvyo0v1QDzXYx8kBZNiCKSXpo0acIHH3yAmXHFFVfQqlUrzjjjDDZs2FDlfbdt25YtW7bgnOO5555j5MiRtGjRgsaNG3Prrbfy6quvAt5Yi2+++WZ4qpFXXnklrqlQjjnmmGIDCZd8xLp2t3PnzmJToISWY426M2TIEP7yl79Qr149jj32WO67776kjIKfTEpgIhKoCy+8kDv++tcKv65r166MHTuWdevWsXTpUtavX8/1119f5Xi+++47WrRowcaNG9m1axe9evUKTyh50kknhS9FHHDAAXTt2pUpU6awa9cu3nzzzUDn8mrUqBHbt28PPw8th0bZj7Ry5UrOPffc8DiMy5Yt469//StvvfVWYPHVREpgIhKodevWscGfW6uyfvGLXzBs2LDwKPGVHYR63rx5fPfddxxzzDG0bNmS7Oxsli1bFq4d/fTTT8XGDQzNlfXvf/+bbt26lTnLcUho8NxYj9AguyUdfPDBxTphLFq0iH322Ye99tqrVNmlS5fSpUsXTjzxRDIyMujSpQunnnoq06dPr8RZSV1KYCJS46xcuZJHHnkkPA/Xt99+y4QJE+jrj7Kzzz77sG7duvCUIuXZvn07U6dO5bzzzuPCCy+ke/fuZGRkcMUVV3DDDTfw448/Al7tbMaMPZNjnHfeecycOZNnnnkm7tpXaPDcWI9jjz026usuvvhi/vGPf7B8+XK2bt3KvffeGx7Au6TDDjuML774gv/+97845/jyyy+ZOnVq7erAgRKYiNRAjRs35uOPPw5PmdK3b18OOeQQHnnkEQB+9atfcfDBB9O6devwjM33339/sWlVAE4//XQaN25Mhw4duO+++/j973/PCy+8EN7+0EMPccABB9C3b1+aNGnCCSecwKpVq8Lb27Rpw1FHHcWHH35YbM69gw8+mPHjxyf0PZ900kn88Y9/5Pjjj6djx4507NiRu+++O7z95JNP5v777wdg//33Z8yYMVx33XU0adKE/v37c/bZZ9fYWwSCEuhgvkGINpiviNRcxx13HDu2bGFBROJIpcF8Jblq6mC+IlILHHXUUfzw1VfJDkPSkBKYiATqgQceSOn5wKTm0jUwERFJSaqBiUigzj77bLZu2MB/H3882aFImlECE5FAbd68mR0RN+iKJIqaEEVEJCUpgYlIjRSaz6tRo0bss88+XHLJJeFRMr777jsGDhxIixYtaN++PaNGFZ9mcOHChfTq1YsGDRrQq1cvFi5cmPD4Jk2axNFHH02DBg047rjjim3btGkT/fr1Y6+99qJZs2YcddRR/O9//4u5r/Lej0SnBCYiNdaUKVPYuXMnn376KfPmzePee+8FvPEV99tvPzZs2MBbb73FrbfeyuzZswHIy8tj4MCBXHjhhWzdupWhQ4cycODAuEftiFeLFi24/vrrufnmm0tta9SoEWPGjGHjxo1s3bqVP/3pT5x++ukUFBRE3VdZ70diUwITkUD9+te/pk/PnlXaR7t27Tj55JNZunQpO3fu5N133+W2226jTp06HHrooQwaNIgxY8YA8O6771JQUMD1119PvXr1uO6663DO8d///jcB72aPE044gcGDB9O2bdtS2+rXr0+XLl3IyMjAOUdmZiZbt25ly5YtpcqW934kNiUwEQnUHXfcweVVHMX922+/Zdq0aRx22GHhmdcjRxFyzoUH+l22bBk9evQoNuBvjx49WLZsWdR9P/jgg+HR6KM9qqJHjx7Ur1+fM844g8svv5y99967VJny3o/EpgQmIjXWmWeeSbNmzTjmmGPo378/t956K40bN6Zfv37cc8895OTk8Omnn/LGG2+E5+0qOa8WeHNrxZpX6+abby5z/q6qWLx4Mdu3b+eVV16JOQ9Yee9HYlM3ehEJ1Mknn8z2TZv4XyU6JkyePJkTTjih1Prx48dz9dVX06FDBzp37swFF1zA8uXLgdLzaoE3Gn20ebWqQ/369RkyZAhdu3alZ8+eUUeML+v9SGyqgYlIoHbv3k1Obm5C99mxY0emTp3Kxo0b+fjjj9m8eTN9+vQBvJHiFy9eXKxJbvHixRx88MFR93X//feXOX9XouTn5/NVjDEhy3o/EptqYCKSclasWEH79u2pV68ekyZNYubMmaxYsQLwRr/PzMzkiSeeYMSIETz33HOANwVLNLfeeiu33nprhWMoLCwkPz+fgoICioqKyMnJITMzkzp16jB37lwKCgro06cPhYWFPPHEE2zYsIEjjzyywu9HYlMNTERSzowZM+jcuTPNmzdn1KhRvP3227Rq1QqAunXrMnnyZMaNG0ezZs0YM2YMkydPpm7dugmN4aWXXiI7O5urrrqK999/n+zsbK644goAcnNzufrqq9lrr71o164d06ZN46233gr3WBw/fnyxGmFZ70di03xgIhIozQcmVaH5wEQkaU477TTWrVyZ7DAkDSmBiUigbrrpJs0HJoHQNTAREUlJqoGJSKCiXQMTSQTVwEREJCUpgYlIjVTWdCplTWUCyZ9O5fPPP2fgwIG0atWKFi1acOKJJ7Jq1aqY+8rNzeXSSy+lSZMmtG7dmkcffTTh8aajwBKYmY0xsx/NLOqIlGZ2gZkt9h8fmlnp8VVEpFaLNZ1KWVOZ1ITpVLZt28YZZ5zBqlWr2LBhA3369GHgwIEx93XXXXfxxRdfsHbtWmbPns1f//pX3lbHl3IFWQMbC5R1o8fXQH/nXA/gHmB0gLGISAqLnE4Fyp7KpCZMp9KnTx8uu+wyWrRoQZ06dbjhhhtYtWoVmzdvjrqvcePGcccdd9C8eXO6du3KFVdcwdixYxMabzoKLIE55+YApSe/2bP9Q+fcVv/pXKB9ULGISPIMHjyY//vlL6u0j8jpVMpTk6ZTCZkzZw6tW7dmr732KrVt69atrF+/vtggv4ceemjMeGWPmtIL8TJgerKDEJHE++1vf1vp+8DOPPNMsrKyaNq0KaeeempcYxZWZjqVaM2AibJu3TquvvrqmNe1Qtf1ImMuK17ZI+kJzMyOx0tg0SfL8coMB4YD7LvvvtUUmYgkwq5du8jJyanUa2NNp1KWmjSdysaNGxkwYAC//e1vGTJkSNQyoRHvt2/fTv369cPLyZr+JZUktReimfUAngcGOueiNw4DzrnRzrnezrneGuBSJLWccsopXPfnP1fb8WrKdCpbt25lwIABnHHGGdx2220xyzVv3pw2bdqwaNGi8LpFixbFjFf2SFoCM7N9gX8CFznnPk9WHCKSegoLC8nJySk2lUl+fj5QfDqV3NxcnnzySaDs6VR27twZ81GZGLZv386JJ55Iv379ePDBB8t9PxdffDH33nsvW7duZeXKlTz33HMMGzasgmel9gmyG/0E4COgi5mtM7PLzGyEmY3wi/wZ2At42swWmpmGmBeRuJQ1lUlNmE7lX//6F/PmzeOFF14oVpv75ptvgNLTqdx9993sv//+dOzYkf79+/OHP/yBkzRaf7k0nYqIBErTqUhVlDWdikbiEBGRlJT0Xogikt6GDRvGmiVLkh2GpCElMBEJ1LBhwzQfmARCTYgiEqhNmzax7aefkh2GpCHVwEQkUIMGDdJ8YBII1cBERCQlKYGJiEhKUgITEZGUpAQmIiIpSZ04RCRQV111FV8tXJjsMCQNKYGJSKDOPfdcFpSYn0skEdSEKCKB+vbbb/lh48ZkhyFpSDUwEQnURRddxI4tWzhV94FJgqkGJiIiKUkJTEREUpISmIiIpCQlMBERSUnqxCEigbrxxhtZvWBBssOQNKQEJiKBOv3001lQp06yw5A0pCZEEQnUqlWrWLNuXbLDkDSkGpiIBOrKK69kx5YtnK37wCTBVAMTEZGUpAQmIiIpSQlMRERSkhKYiIikJHXiEJFA3X777Xw+b16yw5A0pAQmIoE64YQTaF5QkOwwJA2pCVFEArVw4UJWffllssOQNKQamIgE6vrrr2fHli2c37dvskORNKMamIiIpCQlMBERSUlKYCIikpICS2BmNsbMfjSzpTG2m5k9YWarzWyxmR0eVCwiIpJ+guzEMRZ4EhgXY/vJwIH+40jgGf9fEUkj999/Pyvnzk12GJKGAktgzrk5ZtapjCIDgXHOOQfMNbNmZtbGOfd9UDGJSPU7+uijqbd9e7LDkDSUzGtg7YBvI56v89eJSBr58MMPWbR8ebLDkDSUzPvALMo6F7Wg2XBgOMC+++4bZEwikmC33norO7Zs4dL+/ZMdiqSZZNbA1gEdIp63B9ZHK+icG+2c6+2c692qVatqCU5ERGq2ZCawN4GL/d6IfYGfdP1LRETiFVgToplNAI4DWprZOuBOoA6Ac24UMA04BVgN7AIuCSoWEUkPS+bOJW/bNgDqNmtGdw1PVasF2QtxSDnbHXB1UMcXkfSTt20bvfzLCAs2bkxyNJJsGsxXRAL12GOPsfx//0t2GJKGlMBEJFA9e/ak8Icfkh2GpCGNhSgigfrPf/7Dx599luwwJA0pgYlIoO69917+MWFCssOQNKQEJiIiKUkJTEREUpISmIiIpCQlMBERSUnqRi8igXr22WdZ+v77yQ5D0pASmIgEqkuXLuz8+utkhyFpSE2IIhKoKVOmMEczMksAlMBEJFCPPPIIL//zn8kOQ9KQEpiIiKQkJTAREUlJSmAiIpKSlMBERCQlxdWN3swOcc4tDToYEUk/L730EovffTfZYUgaircGNsrMPjGz35pZsyADEpH00qFDB1r7syiLJFJcCcw5dwxwAdABmG9mr5jZ/wUamYikhYkTJzLzvfeSHYakobivgTnnvgBuB/4E9AeeMLOVZvaboIITkdT3zDPP8PpbbyU7DElDcSUwM+thZiOBFcCvgNOdc1395ZEBxiciIhJVvGMhPgk8B9zqnNsdWumcW29mtwcSmYiISBniTWCnALudc4UAZpYB1HfO7XLOvRRYdCIiIjHEew3sP0B2xPMG/joREZGkiLcGVt85tzP0xDm308waBBSTiKSR119/nUWzZiU7DElD8dbAfjazw0NPzKwXsLuM8iIiALRs2ZJmTZsmOwxJQ/HWwK4HXjOz9f7zNsC5gUQkImll7NixrFmyhF7nn5/sUCTNxJXAnHPzzOwXQBfAgJXOufxAIxORtDB27Fh2bNnCXUpgkmDx1sAAjgA6+a85zMxwzo0LJCoREZFyxDuY70vA/sBCoNBf7QAlMBGJaffu3RQVFSU7DElT8dbAegPdnHMuyGBEJH045ygoKEBfGxKUeHshLgVaBxmIiKSXwsLC8LKSmAQh3hpYS2C5mX0C5IZWOufOKOtFZnYS8DiQCTzvnHuwxPamwMvAvn4sf3POvRB/+CJSU4US2BtvvMHyOXOSHI2ko3gT2F0V3bGZZQJPAf8HrAPmmdmbzrnlEcWuBpY75043s1bAKjMb75zLq+jxRKRmKSoqwsxo1KgR9erVS3Y4kobinQ/sPWANUMdfngd8Ws7L+gCrnXNf+QnpVWBgyV0Djc3MgEbAFqAg/vBFpKZyzpGRkcFzzz3HG9OmJTscSUPxTqdyBfA68Ky/qh0wuZyXtQO+jXi+zl8X6UmgK7AeWAL8zjmnLksiaaCoqIiMjAzeeOMNZn3wga6DScLF24njaqAfsB3Ck1vuXc5rLMq6kp/gE/G65rcFegJPmlmTUjsyG25m881s/saNG+MMWUSSxTmHcw4zw2tgKf2fX6Sq4k1guZHXpcwsi/I/j+uADhHP2+PVtCJdAvzTeVYDXwO/KLkj59xo51xv51zvVq1axRmyiCRLqLaVkZFRap1IosSbwN4zs1uBbDP7P+A1YEo5r5kHHGhm+5lZXeA84M0SZb4Bfg1gZvvgDVX1VbzBi0jNFEpWodpX5DqRRIk3gd0MbMS7TnUlMA0ocyZm51wBcA0wA1gBTHLOLTOzEWY2wi92D3C0mS0BZgF/cs5tqvjbEJGaJDKBqQlRghLvYL5FwHP+I27OuWl4yS5y3aiI5fXAgIrsU0RqvsgENnv2bD6cMkU1MEm4eMdC/JooP6Ccc50THpGIpLzQ+IeqgUmQKjIWYkh94BygReLDEZF0ENkD8W9/+xtfL1tGryuvTHZYkmbivZF5c8TjO+fcY8Cvgg1NRFJVKIEBTJ06lY/mz1cToiRcvE2Ih0c8zcCrkTUOJCIRSXmRCSy8LkmxSPqKtwnxkYjlArxhpQYnPBoRSQuhYaREghRvL8Tjgw5ERNJH1BqYmhAlweJtQvx9Wdudc48mJhwRSQeRCSw7O5vcunWVwCThKtIL8Qj2jKRxOjCH4oP1ioiUSlTTp09n7tSpugYmCVeRCS0Pd87tADCzu4DXnHOXBxWYiKS2yCbE8L1gUZoWRSor3qus+wKRk0zmAZ0SHo2IpLyS4yDec889jJkwIZkhSZqKtwb2EvCJmf0LrzfsWcC4wKISkZQVSmDL5s2jcPt2Jr/2Gjm7dsFVV6kGJgkVby/E+8xsOnCsv+oS59xnwYUlIqkqlMDyt23jiH32oXGdOuwuLPS2JTMwSTsVuVGjAbDdOfc4sM7M9gsoJhFJYeFOHFFqWuqJKIkUVwIzszuBPwG3+KvqAC8HFZSIpK7wNbAkxyHpL94a2FnAGcDPEJ4GRUNJiUgpJTtx7NW0KY0aNPC2JS0qSUfxduLIc845M3MAZtYwwJhEJIWV7KjxxsMPM2HatPA2kUSJtwY2ycyeBZqZ2RXAf6jg5JYiUjsoSUl1KbcGZt5PqYnAL4DtQBfgz865dwKOTURSVGQN7JYnn2T5l19y2q9+pSZESahyE5jfdDjZOdcLUNISkTKVbEL8aPFiftyyJbQxSVFJOor3GthcMzvCOTcv0GhEpEZbMncuedu2hZ/XbdaM7n37FisTayoVQ504JLHiTWDHAyPMbA1eT0TDq5z1CCowEal58rZto1erVuHnCzZuLFUm5mgbGoFDEqzMBGZm+zrnvgFOrqZ4RCTFxUpg/q/e6g9I0lZ5NbDJeKPQrzWzN5xzZ1dDTCKSokreAwbQfp99KCgo8LYnJSpJV+UlsMifUZ2DDEREUl+0BPbyPffw6vTpGsRXEq68+8BcjGURkVKiJbAQNSFKopVXAzvUzLbjffay/WXY04mjSaDRiUhKiZagrn/kEVatWcPAX/86CRFJOiszgTnnMqsrEBFJH5E1sIWrVvHjli2YmWpgklAVmU5FRKRMZTUhRm4XSQQlMBFJmDKvgakThySYEpiIJEx5nTgiy4hUVbwjcYiIlCvaTcwHdexIZpShpUSqSglMRBImWgIbfdttxe4DU/1LEiXQn0VmdpKZrTKz1WZ2c4wyx5nZQjNbZmbvBRmPiAQr5jiIxQtVTzCS9gJLYGaWCTyFN45iN2CImXUrUaYZ8DRwhnPuYOCcoOIRkeBFS2DD77uP5/797z3XwKo/LElTQdbA+gCrnXNfOefygFeBgSXKnA/80x8wGOfcjwHGIyIBi9ZB4/O1a/l+06Y9TYiqgUmCBJnA2gHfRjxf56+LdBDQ3MzeNbMFZnZxtB2Z2XAzm29m8zdGmb5BRGoOdZeX6hJkAov2KS750ysL6AWcCpwI3GFmB5V6kXOjnXO9nXO9W0XMRSQiNUtZ18DUhCiJFmQvxHVAh4jn7YH1Ucpscs79DPxsZnOAQ4HPA4xLRAJQ3igcmtBSEi3IGtg84EAz28/M6gLnAW+WKPNv4FgzyzKzBsCRwIoAYxKRgMRKYD27dKFjmza6kVkSLrAamHOuwMyuAWYAmcAY59wyMxvhbx/lnFthZm8Di4Ei4Hnn3NKgYhKR4MRKYI/deCOvTp++p1y1RiXpLNAbmZ1z04BpJdaNKvH8YeDhIOMQkeCV14QYXq8amCSIxncRkYSIlcAuvOMOnnztNW8bqoFJ4iiBiUhCxEpg6zZsYMt2fy5cdeSQBFICE5GEKLcXIuGp3KspIkl3SmAikhBxjYMokkBKYCKSEPEkMDPTNTBJGCUwEUmIWAnsqB49OLBDhyivEKkaJTARSYhYCeyBa65hyIABgK6BSWIpgYlIQugamFQ3JTARSYhYCezsP/yBkRMmAP41MNXAJEGUwESkysrqQr/5p5/YsWvXnrLVFpWkOyUwEamyeO4Bi2e7SEUogYlIlcWdwEqUF6kKJTARqbJ4E5hIIimBiUiVlZXAft2nD4d07hxzu0hlKYGJSJUVFRUB0RPUHZdfzm+OP77YOjUgSiIogYlIlVX0GpjmBJNECHRCSxGpHZxzrPz0UzJ27w6v+3rFCnq1asXJ113H9xs3ct7JJycxQklHSmAiUmXOOQq2b+eotm3D676YPx+A3Tk55OXnA3tqaKp/SSKoCVFEqsw5p8kqpdopgYlIlRUVFRFP+lKKk0RSAhORKqnQPWChMurEIQmgBCYiVVJeAjvt2GM5vEsXr0zoNdURmKQ9JTARqZLwPWAxtt900UWcdswx1ReQ1BpKYCJSJRVpQtRIHJJI6kYvIlVS1igcAMcNH86PW7YUuw9Mg/lKIqgGJiJVUlRUREZG/F8lqoNJoiiBiUiVFBUVVaxp0EydOCQhlMBEpEpUA5NkUQITkUoLXcuqUAIz031gkhDqxCEilVbkJ6LMzMyYZQb/3/8xf9my6gpJahElMBGptHhqYL895xxebdQo/NzQjcySGIE2IZrZSWa2ysxWm9nNZZQ7wswKzWxQkPGISGIVOYeZldmJY1dODrl5edUYldQWgSUwM8sEngJOBroBQ8ysW4xyDwEzgopFRIJRVFRUZvMhwCnXXcdDL70Ufq6bmSVRgqyB9QFWO+e+cs7lAa8CA6OUuxZ4A/gxwFhEJMFCNzBnZVX8SoRuZJZECDKBtQO+jXi+zl8XZmbtgLOAUQHGISIBKPATWHk1sJJU/5JECTKBRfuclvzZ9RjwJ+dcYZk7MhtuZvPNbP7GjRsTFZ+IVEGBfwNzRbrQA5r4UhImyF6I64AOEc/bA+tLlOkNvOq3ibcETjGzAufc5MhCzrnRwGiA3r17q+1BJMmccxQWFZFZ0eRFxJQqfgcQkcoKMoHNAw40s/2A74DzgPMjCzjn9gstm9lYYGrJ5CUiNU+o+TAjjgQ07PTT+Xjx4qBDkloosATmnCsws2vwehdmAmOcc8vMbIS/Xde9RFJUQWFh3M2Hw04/nfoRHT1CtS7VwKSqAr2R2Tk3DZhWYl3UxOWcGxZkLCKSGM45CoqKqBtn541N27ax/eefA45KaiONhSgiFVIY6j4fZwIb9Mc/8tirr4afR14DE6kKJTARqZCKXP8qixKYVJUSmIhUSKj3YWWvX+m6lySKEpiIxM05R5Fzleo+H1LZJsSioiJy8/MpKCzztlGpRZTARCRuoaSTWZVaVCVfm5ubi3OOnPz8yh9b0oqmUxGRuIXn/6pADeyqQYP4cOHC8PPK1MCccxQUFHjL7BmHUWo3JTARiZtzDqNi17HOHTAAV8Vmv6ISPR8L1QFEUBOiiFSAc67CYx9++8MPbP7pp/DzyBuZ4xVKYBkZGRh7uvJL7aYEJiJxK3Kuwt3nL/rzn3nq9derdNxCvwaX4Y/+UaQamKAEJiJxCtWYqnr/V8n9xVs2VPPLMFMCE0AJTETiFGrGS8R9XGZW4SbEUAILvVY3QosSmIjEpShBI3BU9tihxJlRiWtokp6UwEQkLolMYEb8CSjcdBnRhAilZ8eV2kfd6EUkLqHpTyrahHjjhRcyZ8GCSh83sgciRDRhqgZW6ymBiUhcioqKqEzd6/Rf/pKfS06nUoFrYKFyocQVvhG6ErFIelEToojEJfI6VEWsWrOG9Rs3FltXmSbEcAIzq9DrJX0pgYlIXCo7g/KV99/P82++WenjRuv9aGaqgYkSmIiUL4ixBytSAyt57S2jgt3wJT0pgYlIuUo241VVRYaTilbzq+h9ZJKelMBEpFzhBJakY0dLYKFtUnupF6KIlCuRo3BA8SlVyttntAGEI29mjiemJXPnkrdtGwB1mzWje9++FY5Zah4lMBEpV1WaEG+/7DJmz5tXfGUFalBFRUVkZRX/qqronGJ527bRq1UrABaU6BEpqUsJTETKVdku9AAnHHkkm7ZsqdRrYyVONSEK6BqYiMShMvOAhSxctYo1339fbF28NahYCSzUhKiZmWs3JTARKVdl7wEDuP6RRxg3bVrxlXHWoFQDk7IogYlIuSKnM0mEeFNhWdfe1JVelMBEpEyJvgcs2r5jKTmQb6ztUjspgYlImRLdhT5SZZsQQ+tUA6vdlMBEpEwl5+NKhHiTYVnX3jSgr6gbvYiUqapNiPdffTX/mTu31Pp4alBlJjDVwGo9JTARKVNVmxCPPvRQvlm/vlKvjXekjiCaN6XmUxOiiJSpsjMxh3y4aBGff/NNqfXx1KDKuoHadC9YrRdoAjOzk8xslZmtNrObo2y/wMwW+48PzezQIOMRkYqrag3n1qee4tV33im1Pt4mxFjX3io6nJSkn8ASmJllAk8BJwPdgCFm1q1Esa+B/s65HsA9wOig4hGRykn0PWDxKvfam25mrvWC/FT2AVY7575yzuUBrwIDIws45z50zm31n84F2gcYj4hUQlDXmMqrgZWXwFQDkyATWDvg24jn6/x1sVwGTA8wHhGpIOdczU1gqoHVekH2Qoz2qYv6STOz4/ES2DExtg8HhgPsu+++iYpPRMoRxD1g0fYfTXmjcIS2qRNH7RVkDWwd0CHieXugVF9aM+sBPA8MdM5tjrYj59xo51xv51zvVv6cPiISvEQMI/XYjTdy8SmnlFofT/f48srpXrDaLcgENg840Mz2M7O6wHnAm5EFzGxf4J/ARc65zwOMRUQqIZ5aUHl6dulCpzZtSq0vrwlQCUzKE1gCc84VANcAM4AVwCTn3DIzG2FmI/xifwb2Ap42s4VmNj+oeESk4hJRA/vPxx+z5MsvS62PJ4GVd1wlsNot0JE4nHPTgGkl1o2KWL4cuDzIGESk8hIxkO+9//gHP27Zwn3XXFNsfSITmEbjqJ00EoeIxFSVmZjLU14CK2sUjpBQbKqF1U5KYCISU5A1m3hqYOUlT3Wlr92UwEQkpmSNwgHxNyGGykrtowQmIlEFfW2prOQT77U3JbDaTdOpiEhUibqJ+dlbb+WtOXNKrS8r+cR7bI1IX7upBiYiUSWiCz1Al06daBtjAIJY3eDjPXZomhfVwGonJTARiSoRXegBpsyZw4KVK6Nui5V8KnJsDSdVeymBiUhUiWpCfOTll3nrf/+Luq28Glg8x1YNrPZSAhORqBLVhFiWshJYvMc1M9XAail14hCRqEJd6JfMnUvetm0AfL1iBb0SOKB2WU2I8SawyJuZNRpH7aIamIhEFUoiedu20atVK3q1akXBzz8n9BiRQ0FFqsgIIKFyqoXVPkpgIhJVkMNIhcSqMVW0CRGUwGojNSGKSCmhWlEiEthLf/kLb777btRtkcknMzOz2LEr04QotYtqYCJSSiLmAQvp0Lo1ezVtGnVbtORT0d6PoXvBYtXA8vPzySsooKCwsCJhSwpQAhORUhJ1DxjAxJkz+WjJkqjboo3GUZljx7oXLD8/n5ycHIqKitidn0+hmhnTipoQRaSURN0DBvDM66/z45YtUbdFu35VmWNnZGRQUFBQbJ1zjtzcXDIyMqhXpw4G5JYoI6lNNTARKSXUAzHobunRhoKqTPNlRkYGzrliibCgoADnHPXq1cPMqJuVRWFREUW6VpY2lMBEpJTqnEalZPNfZY4d6gASuZ+8vDwyMjLIyvIamur4ZQp1LSxtKIGJSCkVuZG4qqLVwCp67FDCCyWngoICioqKqFu3brHjZGVkUFhUpB6LaUIJTESKSWQX+niEamCh40Z2qY+XmZGZmRm+DpaXl+clrKzil/lDtbCS18skNakTh4gUk8gu9ACv//Wv/PM//4m5PVpX+socOysri9zcXHJzcyksLAxf+ypWJjMTM6OgoIA6depU+BhSs6gGJiLFhBJYRWtBsbRs1owmDRvG3B7Z/BdqAqxMAqtTp4439JV/7StWgsrwE5hG7kh9SmAiUkyia2Bjp0zhvU8/jbk9cizDqiQwM6NBgwbUq1ePBg0axLyOlqlmxLShJkQRKaawsDCh17/GTpkS8z4wKH79yjlHVlZWpTuQZGRkFOu4EbWMf7z8/Pxyy0rNpgQmIsUUFhZW+/Wh0PWr0HJ1Ha+wsJDl8+aFp4up26wZ3fv2Dfz4khhKYCISlujrX/GqU6dOuEmvOpJnnTp1yMvLIzc3NzxdDMCCjRsDP7YkjhKYiISFrkFVdwILXb+qzuPVrVvXq4WV0ZkjcjJP1c5qHiUwEQkrKCjAzKrtHrBkqlOnDvn5+eQXFFBYVERmlPes2lnNpgQmImGFhYUJr31Ne+IJXpsxI6H7TAQzIzs7GzNjV14edTMzNU5iilECExFgz+C3qz79lMIdO8Lrv16xIlwLqYwG9etTr4b29svIyKBuVhZZGRnkFRaSl5/Pzp07ycrKIjMzU0NO1XDp304gInEJdaIo2L6dXq1ahR8FP/9cpf0+/dprzPz440SEGAgzI7tuXRrWq0dWZma4S39OTg65+fn8nJtLvgYArpFUAxMRnHPk5+eHR7NIpEnvvFPmfWA1RYYZWZmZZGdnA15zapbfnJqTn09efn4gTaxSeYEmMDM7CXgcyASed849WGK7+dtPAXYBw5xzsW/ZF5FA5OTkAOjG3giZmZlkZWbSsF498gsLKXKOXbt2UbduXerWrRvIaP3q9VgxgTUhmlkm8BRwMtANGGJm3UoUOxk40H8MB54JKh4RiS43N5eCggLq1q1bK3ofVkadzEzq1alDVlYWeXl57Nq1K6HzihUVFVFQUMCuzZvp3rw5PVq0IGfLFl2DK0eQNbA+wGrn3FcAZvYqMBBYHlFmIDDOeX+luWbWzMzaOOe+DzAukbQS+pILTUcS+SiKMfdVqPZQWFhIUVERWVlZ1KtXr1rjTjWhXouh62O7du3yaml+h49Q8o9VM4v8OxUVFYXHfiwsLAxvKygsJDc0JUxBATt37iTTvy6XkZFBRkZGeBbr6pqvrSYLMoG1A76NeL4OODKOMu0AJTCRGIqKivg5jo4VKxYsIH/79vAX3XfffUe7du0AqNOkCV179SIzM5O6detqapEKyMrKomHDht49ZPn54SGwKiMjIyOcoDL9Wl6jevVwzlEnK4u6detSWFhIXl5ezH00bty40sdPdRZUFdXMzgFOdM5d7j+/COjjnLs2osxbwAPOuQ/857OAPzrnFpTY13C8JkaALsCqBITYEtiUgP1UB8UajFSKFVIrXsUanFSKNxGxdnTORb2PI8ga2DqgQ8Tz9sD6SpTBOTcaGJ3I4MxsvnOudyL3GRTFGoxUihVSK17FGpxUijfoWIO8YjsPONDM9jOzusB5wJslyrwJXGyevsBPuv4lIiLxCKwG5pwrMLNrgBl43ejHOOeWmdkIf/soYBpeF/rVeN3oLwkqHhERSS+B3gfmnJuGl6Qi142KWHbA1UHGUIaENkkGTLEGI5VihdSKV7EGJ5XiDTTWwDpxiIiIBEl3LYqISEpK6wRmZieZ2SozW21mN0fZbmb2hL99sZkdnow4/Vg6mNlsM1thZsvM7HdRyhxnZj+Z2UL/8edkxOrHssbMlvhxzI+yvUacWzPrEnG+FprZdjO7vkSZpJ5XMxtjZj+a2dKIdS3M7B0z+8L/t3mM15b5Ga+mWB82s5X+3/lfZtYsxmvL/MxUU6x3mdl3EX/rU2K8tlrPaxnxToyIdY2ZLYzx2uo+t1G/r6r9cxvt7v10eOB1HPkS6AzUBRYB3UqUOQWYDhjQF/g4ifG2AQ73lxsDn0eJ9zhgarLPrR/LGqBlGdtrzLkt8Zn4Ae++khpzXoFfAocDSyPW/RW42V++GXgoxvsp8zNeTbEOALL85YeixRrPZ6aaYr0LuCmOz0m1ntdY8ZbY/gjw5xpybqN+X1X35zada2Dhoaycc3lAaCirSOGhrJxzc4FmZtamugMFcM597/yBjJ1zO4AVeKOSpKoac24j/Br40jm3NslxFOOcmwOUHK59IPCiv/wicGaUl8bzGU+oaLE652Y65wr8p3Px7udMuhjnNR7Vfl6h7HjNzIDBwISg44hHGd9X1fq5TecEFmuYqoqWqXZm1gk4DIg2idJRZrbIzKab2cHVG1kxDphpZgvMGymlpJp4bs8j9hdATTmvIfs4/55I/9+9o5Spief4UryadzTlfWaqyzV+c+eYGE1cNfG8HgtscM59EWN70s5tie+rav3cpnMCizbSZckul/GUqVZm1gh4A7jeObe9xOZP8Zq/DgX+Dkyu5vAi9XPOHY43o8DVZvbLEttr1Lk172b6M4DXomyuSee1ImraOb4NKADGxyhS3memOjwD7A/0xBtz9ZEoZWrUefUNoezaV1LObTnfVzFfFmVdpc5vOiewhA1lVV3MrA7eh2G8c+6fJbc757Y753b6y9OAOmbWsprDDMWy3v/3R+BfeM0CkWrUucX7j/2pc25DyQ016bxG2BBqcvX//TFKmRpzjs1sKHAacIHzL3SUFMdnJnDOuQ3OuULnXBHwXIwYasx5BTCzLOA3wMRYZZJxbmN8X1Xr5zadE1hKDWXlt3H/A1jhnHs0RpnWfjnMrA/e329z9UUZjqOhmTUOLeNdxF9aoliNObe+mL9ga8p5LeFNYKi/PBT4d5Qy8XzGA2fexLV/As5wzu2KUSaez0zgSlyHPStGDDXivEY4AVjpnFsXbWMyzm0Z31fV+7mtrl4ryXjg9YT7HK/Hy23+uhHACH/Z8Cbd/BJYAvROYqzH4FWjFwML/ccpJeK9BliG12tnLnB0kmLt7MewyI+npp/bBngJqWnEuhpzXvES6/dAPt6v08uAvYBZwBf+vy38sm2BaWV9xpMQ62q8axqhz+2okrHG+swkIdaX/M/jYrwvzTY14bzGitdfPzb0WY0om+xzG+v7qlo/txqJQ0REUlI6NyGKiEgaUwITEZGUpAQmIiIpSQlMRERSkhKYiIikJCWwFGRmhf6o04vM7FMzOzrB+781kfsrse9hZrbRj3+lmd1QhX29a2a9/eVpFmMU9HL2cb2ZNYh4Xqn9VAfzRs2fWt56M7vXzGaYWb0AYxlrZoNirP/a/2x+bmbjzCyhwzCZWVsze91f7mkxRpQv4/WRn8HQo1sZ5W8t8fzDykVear/HJfr/bm2jBJaadjvnejpv6KNbgAcSvP+oCcy/KTkRn5mJzrmeQD/gNjPrUE75cjnnTnHObavES6/Hu0+sqvupEfzhnPoBZzrncuMonxlAGH/wP5tdgM+A2f4NqwnhnFvvnAslz5549xRV1ET//1DosbyMssX+PzjnEpV0jgOUwKpACSz1NQG2QjjBPGxmS82bG+jccta3MbM5/i/QpWZ2rJk9CGT768abWSfz5vx5Gm/MwA5m9oyZzTdvHqC7Q4GYNyfRQ2b2if84oKzAnXOb8W6CDQ09c6H/uoVm9mzoyzXW8SL5x25pZiMiflV/bWazY+3DzK7Du8FydkS5NeYPI2Vmv/fPy1Lz5xCLOB/P+fuaaWbZof2Z2XLzBop9NUqMnczsffNqzeGas/9L/F0ze928Wul4s/DIICf56z7AG04oJjO7Ee/L/HTn3G4zy/T/7vP8mK6MON5sM3sFWFLO8XuZ2XvmDRI7wyowo4DzjMSbwuZkf38DzOwj//2/Zt5YeqHzfre/fomZ/cJf3z/i7/mZmTX2z+NS85LiX4Bz/e3nmjcPVSv/tRnmzTcV17Bg8fx/8MvtjDiP75nZJPNqmw+a2QX+Z3iJme3vlzvdzD724/+Pme1j3gC4I4Ab/H0fa2atzOwN/+81z8z6xXuua63quMNcj4TfBV+Id+f7SuAnoJe//mzgHbz5dvYBvsFLDrHW38ieUTQygcb+8s6IY3UCioC+EetaRLzmXaCH/3xNxP4uJsocW8Aw4El/eV//fdQHugJTgDr+tqeBi8s53rv4I3xQYj4koA7wPt6XeXkxR75uDdAS6IU3YkNDoBHeCAeH+eejAOjpl58EXOgvrwfq+cvNorz3BkB9f/lAYL6/fJz/d2yP96PyI7yRDurjjXBxIN7IJpNinNPj8H7EfAE0iVg/HLjdX64HzAf288v/DOxXzvHrAB8Crfxy5wJj/OWxwKAosZRaDzyGN9RUS2AO0NBf/yf8+a38836tv/xb4Hl/eQreQLX4f4cs/2+wtOTnyX9+J97AsuANqfRGjM/gRvaMILEQyCaO/w+Rz/3ztg3v/1I94Dvgbn/b74DH/OXmEB404nLgEX/5LiLmJgNeAY6J+L+xItnfNTX9kYWkot3Oa4LDzI4CxpnZIXhfOhOcc4V4g2q+BxxRxvp5wBjzBuWc7JxbGON4a503p1fIYPOmbMjC+8/bDW9IGdgz3uAEYGSM/Z1rZsfjNTFd4ZzLMbNf4yWNef6P/2z2DARa1vFieRz4r3NuSiX3cQzwL+fczwBm9k+8KS3eBL6OOFcL8L5Q8fc33swmE31E+zrAk2bWE+9HyEER2z5x/lh35s262wnY6R/rC3/9y3hJKZrVeF+UA4DX/XUDgB6251pVU7xkmOcf7+tyjr8NOAR4x/+bZOINdVRRodHH++Kd9//5+6uLlyxDQgPCLmBPbfN/wKN+7eefzrl1/mtjGYM3/t5jeFO7vBCj3ETn3DXFgjSL9/9DpHnOH+PTzL4EZvrrlwDH+8vtgYl+7bUu8HWpvXhOALpFvL8mZtbYefNtSRRKYCnOOfeR30TSiujTFBBrvXNujnnTLpwKvGRmDzvnxkUp+nN4R2b7ATcBRzjntprZWLyaQni3MZYjTXTOXeMn37fMLDRz84vOuVuKBV7+8Uoxs2FAR7wxDiu1D2KfS4DIa0uFeMkWvPP4S7xpW+4ws4PdnokeAW4ANgCH4tV0csrYZ+j/ZrxjvW0ALgBmmdlm59xs/z1c65ybEVnQzI4j4m9axvENWOacOyrOGGI5DG9cPAPecc4NiVEuFEP4/TvnHjSzt/CaRuea2QkUP2/FOOe+NbMNZvYr4Ei8cxKXCvx/iBYzeC0VuRHLob/h34FHnXNv+uf+rhj7ygCOcs7tjjfm2k7XwFKcf60gE2+w2jl4tZtM/zrAL4FPYq03s47Aj8655/BGlj7c322+/ys0miZ4X34/mdk++Nc2Ipwb8e9HlME59xHe4Kq/w/uCG2Rme/vvq4UfX3nHK3k+euElqwudN2VGeTHvwJsSvaQ5wJlm1sC8Eb7PwmuSjHXcDKCDnzj+CDTDa/KK1BT43o/rIry/W1lWAvuFrqXgjagfk3Puc7yay8t+LW8GcFXob2lmB/nvJV6rgFb+Dw3MrI5VYLJP81yHV+N9G2+g5H7mXxv1z+1B5exjf+fcEufcQ3hNoL8oUSTa3+954GVgkt/qEG+8lfn/EI+meM2LsGekdigd+0z8H11+PD2rcMxaQTWw1JTtN/OA96t2qHOu0Mz+BRyFNyq1A/7onPuhjPVDgT+YWT5ec9XF/j5HA4vN7FPgtsgDO+cWmdlneNeEvsJr4olUz8w+xvtxVOYXru8hvM4h9wO3480qm4E3IvfVzrm55RyvpGuAFngdM8C7znR5GfsYDUw3s++dc6EmH5xzn/o1tU/8Vc875z7zL75Hk4mXOJri/U1GutK9GZ8G3jCzc4DZlK4FFeM3rQ7Hq6VuAj7Aa9Ir6zXzzOwSvKbO4/GaAj8172RsJPoU77H2lec3Pz7hv68svKa5ZeW89GEzuwPvmt9c4HjnTR2/0a8dT7A9XfxvxxuVPJbr/ebmQmA53mzPkR1JZgM3+/8fHnDOTcR77y8Qu/kQvB90x0Q8/y1e82qZ/x+cc3HX6CLcBbxmZt/hnY/9/PVTgNfNbCBwLXAd8JSZLcY713PwOnpIDBqNXhLGzNbgdarYlOxYpPYy797Akc65Y5MdiwRLNTARSRtmdjNwFRW49iWpSzUwERFJSerEISIiKUkJTEREUpISmIiIpCQlMBERSUlKYCIikpKUwEREJCX9P5We/eh8FBcxAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "percentile = 50                                  # percentile to bootstrap (in percentage)\n",
    "def my_percentile(data):                  # define a function to calculate this specific percentile\n",
    "    return np.percentile(data,percentile)\n",
    "\n",
    "L = 1000; random.seed(a=seed)             # set the number and seed for the bootstrap realizations   \n",
    "por_per_real = bootstrap(por,L,my_percentile) # bootstrap of a specified percentile \n",
    "\n",
    "plt.subplot(131)                          # plot the bootstrap results \n",
    "display_bootstrap(por_per_real,0,20,por,'Bootstrap ' + str(per) + 'th Percentile',lpor,None,my_percentile)\n",
    "plt.subplots_adjust(left=0.0, bottom=0.0, right=3.0, top=1.1, wspace=0.2, hspace=0.2); plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Demonstration of Bootstrap for Uncertainty in the Proportion\n",
    "\n",
    "Let's demonstrate another case for which we have the analytical solution to check our bootstrap results.\n",
    "\n",
    "\\begin{equation}\n",
    "P \\sim \\hat{p} \\pm t_{\\frac{\\alpha}{2},n-1}\\sqrt{\\frac{\\hat{p}(1-\\hat{p})}{n}}\n",
    "\\end{equation}\n",
    "\n",
    "We will a big, ad hoc function to assist with this presentation. More could be done to factor, generalize the code."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "def proportion_bootstrap_viz(zdata,nreal,nbin): \n",
    "    sand_prop_real = []                       # declare an empty list to store the bootstrap realizations of the statistic \n",
    "    shale_prop_real = []\n",
    "    \n",
    "    for k in range(0,L):                      # loop over the L bootstrap realizations\n",
    "        samples = random.choices(zdata, k=len(zdata)) # n Monte Carlo simulations\n",
    "        sand_prop_real.append(samples.count(1)/len(zdata)) # calculate the statistic of interest from the new bootstrap dataset\n",
    "        shale_prop_real.append(samples.count(0)/len(zdata)) # calculate the statistic of interest from the new bootstrap dataset       \n",
    "    sand_prop = np.sum(zdata == 1)/len(zdata); shale_prop = np.sum(zdata == 0)/len(df)\n",
    "    \n",
    "    fig = plt.subplot(121) \n",
    "    barlist = plt.bar(x=['shale','sand'],height = [1-sand_prop,sand_prop],color = 'red',alpha = 0.3,edgecolor='black')\n",
    "    barlist[0].set_color('blue')\n",
    "    plt.text(-.3, shale_prop+0.02, r'Prop. Shale = ' + str(round(shale_prop,2)), fontsize=12)\n",
    "    plt.text(0.7, sand_prop+0.02, r'Prop. Sand = ' + str(round(sand_prop,2)), fontsize=12)\n",
    "    plt.xlabel('Rock Type / Facies'); plt.ylabel('Frequency'); plt.title('Facies Histogram')\n",
    "    plt.ylim([0,1]); plt.yticks(np.arange(0, 1.1, 0.1))\n",
    "    \n",
    "    plt.subplot(122)     \n",
    "    plt.hist(sand_prop_real,color = 'red',alpha = 0.2,edgecolor = 'black',bins=np.linspace(0.0,1.0,nbin),label = 'sand',density = True) # plot the distribution, could also calculate any summary statistics\n",
    "    analytical = t.pdf(np.linspace(0.0,1.0,200), len(df)-1,loc=sand_prop,scale=math.sqrt(sand_prop*(1.0-sand_prop)/len(df)))\n",
    "    plt.plot(np.linspace(0.0,1.0,200),analytical,color = 'black',label = 'analytical',alpha=0.4)\n",
    "    plt.axvline(x=sand_prop,linestyle=\"--\",c='black')\n",
    "    plt.fill_between(np.linspace(0.0,1.0,200), 0, analytical, where = np.linspace(0.0,1.0,200) <= np.percentile(sand_prop_real,10), facecolor='red', interpolate=True, alpha = 0.5)\n",
    "    plt.fill_between(np.linspace(0.0,1.0,200), 0, analytical, where = np.linspace(0.0,1.0,200) >= np.percentile(sand_prop_real,90), facecolor='red', interpolate=True, alpha = 0.5)\n",
    "    plt.text(np.average(sand_prop_real)-0.3, 7.0, r'E{Prop.} = ' + str(round(sand_prop,2)), fontsize=12)\n",
    "    plt.text(np.average(sand_prop_real)-0.3, 6.6, r'P90 = ' + str(round(np.percentile(sand_prop_real,90),2)), fontsize=12)\n",
    "    plt.text(np.average(sand_prop_real)-0.3, 6.2, r'P10 = ' + str(round(np.percentile(sand_prop_real,10),2)), fontsize=12)\n",
    "    \n",
    "    plt.hist(shale_prop_real,color = 'blue',alpha = 0.2,edgecolor = 'black',bins=np.linspace(0.0,1.0,nbin),label = 'shale',density = True) # plot the distribution, could also calculate any summary statistics\n",
    "    analytical_shale = t.pdf(np.linspace(0.0,1.0,200), len(df)-1,loc=shale_prop,scale=math.sqrt(shale_prop*(1.0-shale_prop)/len(df)))\n",
    "    plt.plot(np.linspace(0.0,1.0,200),analytical_shale,color = 'black',alpha=0.4)\n",
    "    plt.axvline(x=shale_prop,linestyle=\"--\",c='black')\n",
    "    plt.fill_between(np.linspace(0.0,1.0,200), 0, analytical_shale, where = np.linspace(0.0,1.0,200) <= np.percentile(shale_prop_real,10), facecolor='blue', interpolate=True, alpha = 0.5)\n",
    "    plt.fill_between(np.linspace(0.0,1.0,200), 0, analytical_shale, where = np.linspace(0.0,1.0,200) >= np.percentile(shale_prop_real,90), facecolor='blue', interpolate=True, alpha = 0.5)\n",
    "    plt.text(np.average(shale_prop_real)+0.07, 7.0, r'E{Prop.} = ' + str(round(shale_prop,2)), fontsize=12)\n",
    "    plt.text(np.average(shale_prop_real)+0.07, 6.6, r'P90 = ' + str(round(np.percentile(shale_prop_real,90),2)), fontsize=12)\n",
    "    plt.text(np.average(shale_prop_real)+0.07, 6.2, r'P10 = ' + str(round(np.percentile(shale_prop_real,10),2)), fontsize=12)\n",
    "    \n",
    "    plt.xlabel('Boostrap Realizations and Analytical Sampling Distributions'); plt.ylabel('Frequency'); plt.title('Distribution of Bootstrap Proportions')\n",
    "    plt.legend(loc = 'upper left')\n",
    "    \n",
    "    return sand_prop_real, shale_prop_real"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Demonstration of Bootstrap for Uncertainty in the Proportion\n",
    "\n",
    "Here's the original facies, categorical feature normalized histrogram and bootstrap uncertainty in proportions.\n",
    "\n",
    "* given enough bootstrap realizations, we have a good match with the analytical prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sand_prop_real,shale_prop_real = proportion_bootstrap_viz(zdata=df['Facies'].values,nreal=10000,nbin=20)\n",
    "plt.subplots_adjust(left=0.0, bottom=0.0, right=2.0, top=1.2, wspace=0.2, hspace=0.2); plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Optional: Write out the Bootstrap Samples for an Uncertainty in the Proportions Model\n",
    "\n",
    "Set the file name and feature name and write your bootstrap realizations as a .csv file.\n",
    "\n",
    "* file will be written in the current working directory"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "filename2 = 'prop_facies_boot.csv'        # specify file and feature name for writing out\n",
    "featurename2 = ['sand_prop_boot','shale_prop_boot']\n",
    "\n",
    "df_prop_boot = pd.DataFrame(data=np.column_stack((sand_prop_real,shale_prop_real)),columns=[featurename2]) # make a DataFrame with the bootstrap realizations\n",
    "df_prop_boot.to_csv(filename2,index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Comments\n",
    "\n",
    "This was a basic demonstration of bootstrap.Remember:\n",
    "\n",
    "* you can use bootstrap to calculate uncertainty for any statistic!\n",
    "\n",
    "* you are calculating realizations of the statistic, representing uncertainty due to small sample size\n",
    "\n",
    "* note the assumptions of bootstrap, including stationarity and representativity\n",
    "\n",
    "* remember, get a dataset realization by bootstrap and then calculate the realization of the statistic from the dataset realization\n",
    "\n",
    "* if your statistic has multiple inputs (e.g. P25 and P75), calculate each from the same bootstrap realization of the dataset.\n",
    "\n",
    "I have other demonstrations on the basics of working with DataFrames, ndarrays, univariate statistics, plotting data, declustering, data transformations, trend modeling and many other workflows available at https://github.com/GeostatsGuy/PythonNumericalDemos and https://github.com/GeostatsGuy/GeostatsPy. \n",
    "  \n",
    "I hope this was helpful,\n",
    "\n",
    "*Michael*\n",
    "\n",
    "#### The Author:\n",
    "\n",
    "### Michael Pyrcz, Associate Professor, University of Texas at Austin \n",
    "*Novel Data Analytics, Geostatistics and Machine Learning Subsurface Solutions*\n",
    "\n",
    "With over 17 years of experience in subsurface consulting, research and development, Michael has returned to academia driven by his passion for teaching and enthusiasm for enhancing engineers' and geoscientists' impact in subsurface resource development. \n",
    "\n",
    "For more about Michael check out these links:\n",
    "\n",
    "#### [Twitter](https://twitter.com/geostatsguy) | [GitHub](https://github.com/GeostatsGuy) | [Website](http://michaelpyrcz.com) | [GoogleScholar](https://scholar.google.com/citations?user=QVZ20eQAAAAJ&hl=en&oi=ao) | [Book](https://www.amazon.com/Geostatistical-Reservoir-Modeling-Michael-Pyrcz/dp/0199731446) | [YouTube](https://www.youtube.com/channel/UCLqEr-xV-ceHdXXXrTId5ig)  | [LinkedIn](https://www.linkedin.com/in/michael-pyrcz-61a648a1)\n",
    "\n",
    "#### Want to Work Together?\n",
    "\n",
    "I hope this content is helpful to those that want to learn more about subsurface modeling, data analytics and machine learning. Students and working professionals are welcome to participate.\n",
    "\n",
    "* Want to invite me to visit your company for training, mentoring, project review, workflow design and / or consulting? I'd be happy to drop by and work with you! \n",
    "\n",
    "* Interested in partnering, supporting my graduate student research or my Subsurface Data Analytics and Machine Learning consortium (co-PIs including Profs. Foster, Torres-Verdin and van Oort)? My research combines data analytics, stochastic modeling and machine learning theory with practice to develop novel methods and workflows to add value. We are solving challenging subsurface problems!\n",
    "\n",
    "* I can be reached at mpyrcz@austin.utexas.edu.\n",
    "\n",
    "I'm always happy to discuss,\n",
    "\n",
    "*Michael*\n",
    "\n",
    "Michael Pyrcz, Ph.D., P.Eng. Associate Professor The Hildebrand Department of Petroleum and Geosystems Engineering, Bureau of Economic Geology, The Jackson School of Geosciences, The University of Texas at Austin\n",
    "\n",
    "#### More Resources Available at: [Twitter](https://twitter.com/geostatsguy) | [GitHub](https://github.com/GeostatsGuy) | [Website](http://michaelpyrcz.com) | [GoogleScholar](https://scholar.google.com/citations?user=QVZ20eQAAAAJ&hl=en&oi=ao) | [Book](https://www.amazon.com/Geostatistical-Reservoir-Modeling-Michael-Pyrcz/dp/0199731446) | [YouTube](https://www.youtube.com/channel/UCLqEr-xV-ceHdXXXrTId5ig)  | [LinkedIn](https://www.linkedin.com/in/michael-pyrcz-61a648a1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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